Teaching basic lab skillsfor research computing

A Common Sense Review of a Software Carpentry Workshop

This Fall, I am teaching graduate-level biostatistics. I have not
had the good fortune of teaching many graduate-level offerings, and
I am really excited to do so. A team of top-notch big data
scientists are hosted
at NCEAS. They have
recently formed a really exciting collaborative-learning collective
entitled ecodatascience. I
was also aware of the mission
of Software Carpentry
but had not reviewed the materials. The ecodatascience collective
recently hosted
a carpentry
workshop, and I attended. I am a parent and
use common sense
media as a tool to decide on appropriate content. As a tribute
to that tool and the efforts of the ecodatascience instructors, here
is a brief common sense review.

ecodatascience software carpentry workshop Spring 2016

What You Need to Know

You need to know that the materials, approach, and teaching provided
through software carpentry are a perfect example of contemporary,
pragmatic, practice-what-you-teach instruction. Basic coding skills,
common tools, workflows, and the culture of open science were
clearly communicated throughout the two days of instruction and
discussion, and this is a clear 5/5 rating. Contemporary ecology
should be collaborative, transparent, and reproducible. It is not
always easy to embody this. The use of GitHub and RStudio
facilitated a very clear signal of collaboration and documented
workflows.

All instructors were positive role models, and both men and women
participated in direct instruction and facilitation on both days.
This is also a perfect rating. Contemporary ecology is not about
fixed scientific products nor an elite, limited-diversity set of
participants within the scientific process. This workshop was a
refreshing look at how teaching and collaboration have
changed. There were also no slide decks. Instructors worked directly
from RStudio, GitHub Desktop app, the web, and gh-pages pushed to
the browser. It worked perfectly. I think this would be an ideal
approach to teaching biostatistics.

Statistics are not the same as data wrangling or coding. However,
data science (wrangling & manipulation, workflows, meta-data,
open data, & collaborative analysis tools) should be clearly
explained and differentiated from statistical analyses in every
statistics course and at least primer level instruction provided in
data science. I have witnessed significant confusion from
established, senior scientists on the difference between data
science/management and statistics, and it is thus critical that we
communicate to students the importance and relationship between both
now if we want to promote data literacy within society.

There was no sex, drinking, or violence during the course :).
Language was an appropriate mix of technical and colloquial so I
gave it a positive rating, i.e. I view 1 star as positive as you
want some colloquial but not too much in teaching precise data
science or statistics. Finally, I rated consumerism at 3/5, and I
view this an excellent rating. The instructors did not overstate the
value of these open science tools – but they could have and I wanted
them to! It would be fantastic to encourage everyone to adopt these
tools, but I recognize the challenges to making them work in all
contexts including teaching at the undergraduate or even graduate
level in some scientific domains.

Bottom line for me – no slide decks for biostats
course, I will use GitHub and push content out, and I will share
repo with students. We will spend one third of the course on data
science and how this connects to statistics, one third on connecting
data to basic analyses and documented workflows, and the final
component will include several advanced statistical analyses that
the graduate students identify as critical to their respective
thesis research projects.

I would strongly recommend that you attend a workshop model similar
to the work of Software Carpentry and the ecodatascience
collective. I think the best learning happens in these contexts. The
more closely that advanced, smaller courses emulate the workshop
model, the more likely that students will engage in active research
similarly. I am also keen to start one of these collectives within
my department, but I suspect that it is better lead by more junior
scientists.

Net rating of workshop is 5 stars.

Age at 14+ (kind of a joke), but it is a proxy for
competency needed. This workshop model is best pitched to those that
can follow and read instructions well and are comfortable with a
little drift in being lead through steps without a simplified slide
deck.